Development and Validation of Predictive Models for Vaginal Birth After Cesarean Delivery in China

Background: The rate of delivery by cesarean section is rising in China, where vaginal birth after cesarean (VBAC) is in its early stages. There are no validated screening tools to predict VBAC success in China. The objective of this study was to identify the variables predicting the likelihood of successful VBAC to create a predictive model. Material/Methods: This multicenter, retrospective study included 1013 women at >= 28 gestational weeks with a vertex singleton gestation and 1 prior low-transverse cesarean from January 2017 to December 2017 in 11 public tertiary hospitals within 7 provinces of China. Two multivariable logistic regression models were developed: (1) at a first-trimester visit and (2) at the pre-labor admission to hospital. The models were evaluated with the area under the receiver operating characteristic curve (AUC) and internally validated using k-fold cross-validation. The pre-labor model was calibrated and a graphic nomogram and clinical impact curve were created. Results: A total of 87.3% (884/1013) of women had successful VBAC, and 12.7% (129/1013) underwent unplanned cesarean delivery after a failed trial of labor. The AUC of the first-trimester model was 0.661 (95% confidence interval [CI]: 0.61-0.712), which increased to 0.758 (95% CI: 0.715-0.801) in the pre-labor model. The pre-labor model showed good internal validity, with AUC 0.743 (95% CI: 0.694-0.785), and was well calibrated. Conclusions: VBAC provides women the chance to experience a vaginal delivery. Using a pre-labor model to predict success-ful VBAC is feasible and may help choose mode of birth and contribute to a reduction in cesarean delivery rate.